benefits of home currency invoicing -...

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Benefits of Home Currency Invoicing§

Kazunobu HAYAKAWA

Bangkok Research Center, Institute of Developing Economies, Thailand

Nuttawut LAKSANAPANYAKUL

Science and Technology Development Program, Thailand Development Research Institute, Thailand

Taiyo YOSHIMI#

Department of Economics, Nanzan University, Japan

Abstract: We present a model of endogenous choice of import frequency and invoice currency to

infer the benefits of home currency invoicing. In the model, those benefits are represented by fixed

costs of exchange rate risk management paid by importers when they work with foreign currency

invoicing. Using a very detailed dataset on Thai imports, we show that those benefits of average

Thai importer range between 7.3% (1,500 USD) of one-time shipment value in our most

conservative specification and 17.1% (3,600 USD). Those benefits become larger when the share of

the export country currency in daily global foreign exchange market turnover is lower, or the export

country is one of the partners of Thailand’s regional trade agreements. Further, we find that

frequency of shipments is higher and the value per shipment is smaller for transactions priced in

buyers’ currency than those not priced in it. Our model provides a rationale for these empirical

findings.

JEL Classification: F10; F14; F31; F40

Keywords: Invoice currency; Frequency of trade; Shipment

1. Introduction

Running an international currency provides benefits for a country as it liberates

§ We have benefited from discussions with and comments of Shingo Iokibe, Hugh Patrick and David Weinstein. We would also like to thank the seminar participants at The Japan Society of International Economics Kansai, Research Institute of Economy and Industry (RIETI) and Center on Japanese Economy and Business (CJEB), Columbia Business School. All remaining errors are ours. This work was supported by the Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Young Scientists (A) (15H05393). # Corresponding author: Taiyo Yoshimi; Address: Department of Economics, Nanzan University, 18 Yamazato-cho, Showa-ku, Nagoya-shi, Aichi-ken 466-8673, Japan; Tel.: +81-52-832-3111, fax: +81-52-835-1444; Email: [email protected]

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home agents from the risk of exchange-rate fluctuations. There has been a long debate

on this topic, and huge amount of estimations of those benefits has been provided. One

of the major aspects of currency internationalization is trade invoicing. If particular

trade is invoiced in the home currency, home agents do not need to pay attention to

exchange-rate risk. In contrast, when the international transaction is invoiced in foreign

currencies, traders generally utilize some ways of exchange-rate risk management, such

as forward exchange rates and currency options, by paying visible and invisible costs. In

short, currency internationalization contributes to reducing broadly-defined trade costs

for firms in the home country.

In this paper, we quantify the benefits of home currency invoicing (HCI) through

measuring the costs for exchange-rate risk management in the case of foreign currency

invoicing (FCI). Namely, the benefits of currency internationalization in terms of

reducing the trade costs are quantified. To do that, we shed light on the relationship

between trade frequency and uncertainty. As theoretically demonstrated in Bekes et al.

(2014), firms adjust to increased uncertainty by reducing their number of trade

shipments and increasing their size per shipment. In the case of FCI, firms’ future

revenue/payment for every shipment in terms of its own currency is exposed to

unexpected exchange-rate fluctuation. This specific type of uncertainty in FCI motivates

us to focus on the trade-off between HCI and FCI. Suppose that importers are invoicing

their international transactions in terms of a foreign currency. If payments come after

contracts and importers’ home currency is depreciating, future payments in importers’

home currency will become cheaper. This implies one of the advantages of FCI. On the

other hand, FCI requires firms to incur some costs for exchange-rate risk management

in every transaction. Using such trade-off in invoice currency, we identify the costs for

exchange-rate risk management in FCI.

Specifically, we construct a model of endogenous choice of import frequency and

invoice currency to infer those costs. We extend the model developed by Kropf and

Sauré (2014) by two ways. First, we focus on the optimization behavior of importers

rather than exporters as we like to examine how the adoption of buyers’ (importers’)

currency invoicing helps their just-in-time orders and how it is related to the benefits of

HCI. Second, we introduce endogenous choice of invoice currency. As a result, our

model proposes a way to infer the costs of FCI. Further, it also provides two new

propositions on the relationship of the invoice currency with import frequency and the

value per shipment. One is that import frequency of FCI importers is lower than that of

HCI ones under some conditions. This consequence becomes more likely when the

costs of exchange-rate risk management become larger. The other is that the value per

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shipment of FCI importers increases with the costs of exchange-rate risk management.

In the empirical part, we infer the costs for such exchange-rate risk management

by employing the customs import data in Thailand during 2007 to 2011. Our dataset,

which is obtained from the Customs Office, the Kingdom of Thailand, is

transaction-level import data and covers all commodity imports in Thailand. It contains

not only the usual data items such as Harmonized System (HS) eight-digit code or

export country but also import firm identification code, customs clearing date, and

invoice currency. For example, we can see that some firms import the same commodity

from the same country under the same invoice currency multiple times within the same

date. The case of Thailand not only provides us the detailed data necessary for our

analysis but also shows the case of large costs for exchange-rate risk management since

its home currency is not internationalized. Therefore, there are a sufficient number of

import transactions under FCI. By applying these data to our theoretical model, we infer

fixed costs per import shipment in the cases of HCI and FCI. We also examine how such

costs are related to export country characteristics such as turnover of export country

currency. In addition, with this dataset, we empirically investigate the above two

propositions derived from our theoretical model.

Our paper is related to literatures on trade frequency and invoice currency. The

former literature is recently growing. Eaton et al. (2008) is an early micro-level study on

this literature and provides various basic statistics on the number and frequency of

export transactions in Columbia. Alessandria et al. (2010) demonstrate that the existence

of fixed costs per import shipment leads to the lumpiness of import transactions. The

calibration of their inventory model for Chilean import data shows that such fixed costs

amount “to approximately 3.6 percent of the average value of an import shipment”.

Kropf and Sauré (2014), which is the closest paper to ours, also compute fixed costs per

export shipment with Swiss export data and found a similar magnitude. In this paper, by

applying their method, we compute not only fixed costs per import shipment but also

those of exchange-rate risk management in the case of FCI. When importing under

home currency, firms do not need to bear the latter costs. Therefore, those costs are

interpreted as the benefits of HCI. To the best of our knowledge, such costs have never

been quantified by the way driven from micro-founded economic theory using highly

disaggregated dataset.

Furthermore, Hornok and Koren (2014) and Bekes et al. (2014) shed more light

on the correlation of shipment frequency with several variables. The former study found

in the export data in the U.S. and Spain that export shipments are less frequent and

larger when exporting to countries with larger per-shipment costs. Using French export

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data, as mentioned above, the latter study shows that firms adjust to increased

uncertainty by reducing their number of shipments and increasing their shipment size.

Against these studies, our paper sheds light on the correlation of the shipment frequency

and size with a novel element, invoice currency. Nevertheless, as mentioned above, this

analysis will be similar to that in Bekes et al. (2014) because the choice of invoice

currency is related to one specific type of uncertainty, future unexpected exchange-rate

fluctuation. In this sense, our analysis can be compliment to Bekes et al. (2014).

The other related literature is one on the choice of invoice currency. Many

researchers have examined how traders determine the invoice currency and have tried to

endogenize its choice. For instance, Goldberg and Tille (2008) explore the major driving

forces for currency invoicing in international trade with a dataset covering 24 countries.

Further, Goldberg and Tille (2009) examine the choice of the invoice currency through

bargaining between importers and exporters. Based on a highly disaggregated dataset of

Canada, they show that larger transactions are more likely to be invoiced in the

importers’ currency, which is rationally explained by their model of endogenous choice

of invoice currency through a bargaining process. Gopinath et al. (2010) construct a

model of endogenous currency choice where importers choose the invoice currency so

that realized degree of exchange rate pass-through becomes close to its desired degree.

In this paper, on the other hand, we assume that forward exchange premiums are

exogenously offered by financial institutions to each firm and are heterogeneous across

firms. Under this setting, we will show that given the fixed cost of exchange rate risk

management, importers choose FCI when the home currency is significantly discounted

in the suggested forward exchange rate and thus the cost of imported intermediate

inputs is supposed to become large when the payment is settled. The advantage of our

framework is that it provides a way to infer the difference in broadly-defined trade costs

including exchange rate risk between HCI and FCI, which reveals practical benefits of

HCI in international transactions.

The level of disaggregation is also one of the advantages of this study. With our

data, we can derive detailed information including the frequency of transactions, value

per shipment, and the invoice currency for each transaction. Several recent studies also

utilize dataset with a comparable level of disaggregation with ours to examine topics on

invoice currencies. Chung (2014) reveals that exporters’ dependence on imported inputs

affects their choice of invoicing currency using a dataset which covers all United

Kingdom trade transactions with non-European Union (EU) countries. Fabling and

Sanderson (2015) examine how invoice currency is linked to the degree of exchange

rate pass-through with New Zealand’s data. Reiss (2015) uses Brazilian trade data, and

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finds that the Brazil-Argentina policy of providing payment orders associated to an

exchange transaction between their currencies had significant impacts on the choice of

invoice currencies.

Also, our paper is related to a huge literature on the measurement of trade costs

because the costs of exchange-rate risk management for FCI are a part of the

broadly-defined trade costs. Anderson and van Wincoop (2004) is one of the most

influential papers in this literature. Rose and van Wincoop (2001) and the sequential

papers estimate the costs of not sharing the same currency in trading. To do that, these

papers mainly estimate the gravity equation. As a result, in Rose and van Wincoop

(2001), the tariff equivalent of those costs is estimated to 14 percent. Compared with

these studies, we compute the more specific costs when using different currency in

trading, i.e., fixed costs of exchange-rate risk management for FCI. In terms of

measuring the absolute magnitude of fixed costs, our paper is also related to Das et al.

(2007) and Cherkashin et al. (2015). These studies structurally estimated fixed costs for

exporting (around 400 thousand US dollars (USD)) and certifying the origin of exported

goods (around four thousand USD), respectively.

The major results of this paper are summarized as follows. Firstly, the benefits of

HCI for average Thai importer are positive and range between 7.3% (1,500 USD) of

one-time shipment value in our most conservative specification and 17.1% (3,600 USD).

This implies that the adoption of the home currency as an invoice currency provides the

benefits for home importers. Secondly, those benefits become larger when the share of

the export country currency in daily global foreign exchange market turnover is lower,

or the export country one of the partners of Thailand’s regional trade agreements (RTAs).

Thirdly, the frequency of import shipments is higher and the value per shipment is

smaller for transactions priced in importers’ currency than those not priced in it, which

is rationally interpreted by our theoretical model.

The rest of this paper is organized as follows. In Section 2, we present a model of

endogenous choice of import frequency and invoice currency. Section 3 provides a way

to infer the benefits of HCI based on our model. In Section 4, we examine the

propositions on the relations between invoice currency, import frequency, and the value

per shipment. In Section 5, we infer the fixed costs per shipment and the benefits of

HCI, and examine their determinants. Finally, Section 6 concludes the paper.

2. The Model This section presents a partial equilibrium model of endogenous choice of import

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frequency and invoice currency. As we noted, the most closely-related study to ours is

Kropf and Sauré (2014). We extended their model by two ways. Firstly, we focus on the

optimization behavior of importers and, exporters are simply assumed to supply the

amount of intermediate goods upon the request of final-good producers. Secondly, the

choice of invoice currency is endogenous, which helps to figure out the simultaneous

determination of invoice currency and import frequency. Figure 1 present a simplified

image of our model.

=== Figure 1 ===

2.1. Representative Household Suppose the economy which is constructed with infinite number of countries.

Each country is indexed by and countries are distributed continuously in the range

0, 1 . In each country, the representative household, final- and intermediate-good

producers, and financial institutions exist. The representative household purchases final

goods from local producers and consume them. The final-good market is

monopolistically competitive, and each final-good producer supplies particular variety

which is indexed by . The number of final goods is infinite and is assumed to be

distributed in the range 0, 1 .

The representative household has a linear utility function over the consumption as

follows:

.

Here, is the consumption index which is defined by a Dixit-Stiglitz function over

monopolistic-competitive varieties in the following manner:

≡ ,1 ∞, 1

where is the consumption of the variety , and is the elasticity of substitution

between varieties. The optimal allocation of any given consumption of each variety of

goods yields the following demand functions:

, 2

where the consumer price index (CPI) is given by

≡ . 3

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Here, we used the assumption ⁄ which implies that the representative

household is endowed with the exogenous amount of nominal income, , and expenses

it only to current consumption.

2.2. Forward Exchange Rates Each final-good producer makes its output using imported or domestically

produced intermediate inputs. Final-good producers determine the frequency of

transactions with intermediate-good producers. Those which import intermediate inputs

from abroad also determine the invoice currency. Particularly, we consider two

alternative cases of invoicing, HCI and FCI.1 The critical difference between these two

cases is that importers are free from the risk of exchange-rate fluctuations in the HCI

case but they have to manage it in the FCI case. Suppose that home importers and

foreign intermediate-good exporters make contracts of international transactions in the

current period, and the payment will be taken place after ′ periods. If the trade is

invoiced in importers’ (home) currency, they do not have to care about the risk of

exchange rate fluctuations over the gap between contract and payment. On the other

hand, they have to consider how they manage the risk when the trade is invoiced in

foreign currencies.

Let and ′ denote spot exchange rates of home currency in the current

period and after ′ periods, respectively, against the invoice currency which is used for

the trade between home importers and their intermediate-good suppliers. We assume

that each importer manages the risk of exchange rate fluctuations by utilizing the

forward exchange rate with the nominal fixed cost, , when the trade is not invoiced

in their home currency, i.e. in the HCI case. This cost includes the documentation cost

of forward exchange rate contracts, and the transaction cost charged by the financial

institutions. Thus, we interpret as the benefits of adopting the home currency as an

invoice currency in import. Further, we also assume the existence of the fixed costs per

shipment, . We define ′ as the forward exchange rate charged by the financial

institution which serves for the importer .

We assume the heterogeneity in the forward exchange premiums offered by

financial institutions to each company. It is natural to assume such heterogeneity

1 In studies such as Chung (2014) and Fabling and Sanderson (2015), vehicle currency invoicing and producer currency invoicing are identified. The purpose of this study is to reveal the benefits of HCI, which corresponds to local currency invoicing, relative to other two invoicing manners. Consequently, in the theoretical part, we jointly consider vehicle and producer currency invoicing as FCI for simplicity, and focus on the comparison between HCI and FCI. In the empirical part, we identify those two types of FCI.

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because different financial institutions offer different premiums. Further, even the same

bank can offer different premiums depending on the relation with each company. For

instance, if a bank has a long relation with a particular company, it might provide

special discount for future currency exchanges. Or, if the owner of a company has a

knowledge or experience to find a better financial institution, it can make a better

forward exchange contract. We denote the forward premium offered by a financial

institution to each firm by , which is assumed to be constant over time. Thus, the

forward exchange rate is determined in the following manner:

′ . 4

2.3. Final-good Producers Each final-good producer has linear production function over imported

intermediate inputs and the productivity in the following manner:

. 5

Here, is the output of final-good producer , is the productivity, and is the

unit of imported or domestically produced intermediate inputs. In the model, we assume

that each final-good producer imports intermediate inputs from one counterpart for

simplicity, although the data shows that one company imports from multiple countries

in many cases. We can interpret our model so that multiple importers belong to one

company as an importing section and each of them independently determines how to

import each product. This interpretation leads to an economy where one company

imports from multiple countries, which is consistent with the data. We denote the

intermediate-good price denominated in the invoice currency by ∗ ≡ ̃∗. Here, is

the gross tariff rate, and 1 and 1 for final-good producers which use

imported and domestic intermediate inputs, respectively. ̃ ∗ is the mill price. Both

and ̃ ∗ are exogenous. Thus, the nominal marginal cost in the home currency is derived

as follows: ∗

. 6

Final-good producers make their output as soon as they import intermediate inputs. It

should be noted that the invoice currency is the home currency and 1 for

final-good producers which use domestic intermediate inputs.

When there is a time gap between final-good production and the sales, storage

costs take place. Following Kropf and Sauré (2014), we assume the existence of the

gross ad valorem storage costs, . Given the price markup, marginal cost, and

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storage cost, the consumer price of the variety is derived as follows:2

′1

∗

. 7

The operating profit of per unit sales of each final good is derived in the following

manner: ∗

1∗ 1

1

∗

. 8

Combining (2) and (8), the operating profit of total sales at time is obtained as

1

1

∗

. 9

We denote the time between two shipments by ∆ . In other words, each

final-good producer sells their output over ∆ , and makes the order of next shipment

after all the inventory stock is sold out. Normalizing one year to unity, the interval ∆

is expressed as a fraction of years. Consequently, ∆ represents the import frequency

or the number of shipments per year between home country and the origin of imported

intermediate inputs. Taking and as given, the present value per shipment is

derived in the following manner:3

Π ∆ ≡∆ 1 ∆

1, 9

where,

≡1

1

∗

, 10

≡ . 11

Note that Δ ∈ 0,∞ → ∈ 0,1 .

2.4. Optimal Frequency of Shipments Import frequency and invoice currency are simultaneously determined. Firstly,

final-good producers calculate the optimal frequency of shipments in a given period in

HCI and FCI cases. Secondly, they compare present values of profits from all shipments

2 Sum of and can be interpreted as effective storage cost as future depreciation of home currency increases the cost of imported intermediate inputs denominated in the home currency. It is natural to assume 0, which implies storages cannot become beneficial in effect. This leads to a condition , where is the lower bound of . As a result, is supposed to follow a

probability distribution with the range [ , ∞). 3 In Appendix A, we will show that there is a distribution of shipment dates for which becomes constant.

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in both cases, and choose the invoice currency which realizes better outcome. In this

subsection, we show the detail of these optimization processes. In the calculation of

optimal frequency of shipments, final-good producers face the trade-off between storage

costs and fixed costs per shipment. Given the total value of all shipments per period, the

higher frequency of shipments is associated with the larger value per shipment.

Consequently, total amount of fixed costs per shipment and storage costs in a given

period become larger (smaller) and smaller (larger), respectively, if importers make

more (less) frequent shipment orders.

We denote total fixed costs for each shipment charged to the importer by .

Recall that corresponds to and in the HCI and FCI cases, respectively.

Further, importers do not need to utilize forward exchange rates, and disappears

from equations in the case of HCI ( 0). The present value of profits from all

shipments is defined by

≡ Π ∆1

1

1

1 12

Each final-good producer calculates the optimal frequency by determining to

maximize in HCI and FCI cases.

The first order condition is derived as follows:

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1 0 13

Given that the range of is ∈ 0,1 , the left hand side of (13) is proved to be

decreasing in . Further, it becomes positive and negative in the limit of → 0 and

→ 1, respectively, given the necessary condition for final-good producers’ market

entry that 0. Thus, is uniquely determined for both HCI ( ) and FCI ( )

cases (∆ and ∆ , henceforth). Note that the optimal frequency in the HCI case does

not depend on . Further, ( ) is positively related to the frequency 1 ∆⁄ (1 ∆⁄ ).

Two consequences are implied by the first order condition. First, the optimal

frequency is negatively associated with total fixed costs for each shipment, , for both

HCI and FCI cases. Thus, the existence of can be a source of lower frequency in the

FCI case than the HCI case. Second, in the case of FCI, the optimal frequency is

negatively associated with the forward premium, . Higher premium leads to the rise

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of the cost of imported intermediate inputs denominated in the home currency. As a

result, for a final-good producer which faces higher premium, it is more difficult to

cover total costs of each shipment, and thus the frequency becomes lower.

2.5. Choice of the Invoice Currency Combining (12) and (13), the equilibrium value of is written by

. 14

Final-good producers compare the in HCI and FCI cases, and choose the invoice

currency which realizes better consequence. Thus, the condition for HCI invoicing is

, or

. 15

It is straightforward to show that the right hand side of this condition is decreasing in

. Thus, final-good producers with high value of forward premium tend to choose their

home currency as an invoice currency. The condition (15) determines the cutoff forward

premium for which it holds with equality. It is straightforward to show the

uniqueness of given the monotonic decrease of the right hand side of (15) in .

Figure 2 depicts the relation between the value of the forward premium and

choice of the invoice currency. In the vicinity of the cutoff forward premium, import

frequency of FCI importers is lower than that of HCI ones. The FCI curve shifts

downward when the fixed cost of utilizing forward exchange rate, , becomes larger.

This amplifies the likelihood of lower frequency for FCI importers than HCI ones. Thus,

we obtain the proposition on the relation between the invoice currency and frequency of

import shipments as follows:

Proposition 1: Import frequency of FCI importers is lower than that of HCI ones

around the cutoff forward premium, which is more likely when the fixed cost of

exchange-rate risk management is larger.

=== Figure 2 ===

Further, we examine the optimal value per shipment in terms of the home

currency. Combining (2) and (8), it is derived in the following manner:

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≡ ∗∆ 1 1

1 . 16

We found that the optimal frequency becomes higher when the total fixed cost of each

shipment becomes lower. This implies, according to (16), that the existence of can

lead to the larger value per shipment for FCI importers than HCI ones. However, it

depends on parameter values whether the value per shipment for the former is larger

than the latter in sum, even in the vicinity of the cutoff forward premium, as the sign of

the partial derivative of with respect to depends on parameter values. In sum,

we derive the following proposition on the relation between the invoice currency and

the value per shipment:

Proposition 2: The existence of the fixed cost of exchange-rate risk management

increases the value per shipment of FCI importers over that of HCI importers.

2.6. Benefits of HCI By combining (13) and (16) to eliminate , we derive the indirect measure for

the total fixed cost of each shipment as follows:

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≡1 1 1

18

≡ 1 11 1

19

Recall that is and , respectively, for HCI and FCI, and 0 for HCI.

Thu, the indirect measure of is derived by

. 20

Further, by eliminating , we infer the measure for in the following manner:

. 21

As we noted, is interpreted as the benefits of using the home currency as an invoice

currency in international transactions, more simply the benefits of home currency

invoicing.

3. Data Overview This section takes an overview of invoice currency, import frequency, and imports

per shipment in Thailand. As mentioned in the introductory section, our dataset is

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transaction-level import data from 2007 to 2011 and covers all commodity imports in

Thailand. It contains customs clearing date, HS eight-digit code, export country, import

firm identification code, invoice currency, import values in Thai Baht (THB), import

quantity, and quantity unit.4 Since our dataset does not include information on export

firms, we differentiate import transactions according to import firm, HS eight-digit code,

export country, and quantity unit. For example, if a firm has two records on imports of

the same HS code from the same export country under different quantity units, we

regard these two records as showing the import from two different exporters. In addition,

in our dataset, there are some firms in which multiple import transactions are recorded

in the same HS eight-digit commodity, the same export country, the same invoice

currency, the same unit, and the same date. We aggregate these data at a daily basis and

thus regard this case as one transaction.

We start from the sample distributions of the number of shipments per year and

average imports per shipment in 2011, which are depicted in Figures 3 and 4,

respectively. The former figure shows that more than a half of all import transactions

have only one shipment per year. Compared with the case of developed countries shown

in Kropf and Sauré (2014) and Bekes et al. (2014), the share of transactions of one

shipment per year is very high. Also, as in the case of developed countries, the density

naturally decreases with the increase of the shipment number. Figure 4 shows that the

distribution of average imports per shipment seems to follow the log-normal

distribution.

=== Figures 3 and 4 ===

Table 1 reports the share of each type of invoice currency in total import, in terms

of import transactions and import values. The invoice currency includes local currency

(HCI) and non-local currency (FCI). While the former is Thai baht (THB) in our context,

the latter is further decomposed into producer currency (i.e., national currency in export

countries) and vehicle currency (i.e., neither home nor producer currencies). The table

shows that approximately 60% and 80% of import in Thailand are invoiced in the

vehicle currency in terms of the number of transactions and of values, respectively. This

fact is consistent with the well-known one that a significant part of international

transactions in Asia is invoiced in third vehicle currency, particularly, US dollar (USD).

4 We drop the transactions in which the information on invoice currency, export country, or quantity unit is missing. Therefore, the aggregated figures of our data may not be consistent with the official figures on imports.

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The table also shows that the local currency (i.e., THB) invoicing is relatively minor to

vehicle and producer currency invoicing. Its share is less than 10 percentages in our

sample in terms of both the number of transactions and the values.

=== Table 1 ===

Shares of invoice currencies vary across sectors and regions. Table 2 presents the

share of each type of invoice currency by industry, in terms of the number of import

frequency and the import values. In almost all industries, as in the case of Table 1, the

local currency has the lowest share. In those industries, the share of vehicle currency is

higher than that of producer currency in terms of both the number of import frequency

and the import values. In particular, footwear and textile industries have significantly

high share of vehicle currency. In addition, the extremely high share of vehicle currency

in mineral products in terms of values reflects the fact that the prices of such mineral

products are determined in the international market in USD. On the other hand, the high

share of local currency can be found only in art products probably because of the strong

bargaining power of importers (buyers) in this industry.

=== Table 2 ===

Table 3 shows the variety of invoice currencies across exporter continents. In all

continents, local currency plays a minor role as an invoice currency. When importing

from Africa, the share of vehicle currency, maybe either USD or euro (EUR), is

significantly high. The high share of producer currency in importing from America will

be due to the frequent use of USD in importing from the U.S. The similar applies to the

high share of vehicle currency in Asia. The significant share of producer and vehicle

currencies in Europe will be due to the use of EUR and USD, respectively. The

observations in Pacific are interesting. Not only vehicle currency but also producer

currency have the high share in terms of frequency. Namely, Australian dollar (AUD)

and New Zealand dollar (NZD) play some role in invoicing in Thai import from Pacific.

=== Table 3 ===

Next, Table 4 shows the basic statistics on the import transaction-level frequency

and average values according to invoice currencies. As shown in Figure 2, regardless of

invoice currencies, the median of import frequency is one, implying that most of Thai

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importers trade once a year. In maximum cases, importers trade every day and the

frequency reaches even to 365 times in a year. The mean of frequency in imports

invoiced in local currency is significantly highest among three types of invoicing. On

the other hand, the mean of imports per shipment shows the reverse order. While the

mean in vehicle currency is largest, the case of local currency has the smallest mean of

imports per shipment. The same is true for the median of imports per shipment. Also,

the standard deviation of values per shipment in addition to their maximum is

significantly large in the case of vehicle currency.

=== Table 4 ===

The means of import frequency and imports per shipment are reported by invoice

currency and industry in Table 5. There are four noteworthy points. First, the means of

import frequency and imports per shipment are significantly different across industries.

For example, live animals, vegetable products, and transport equipment have relatively

high frequency. Second, in almost all industries, the mean of import frequency is highest

in local currency invoicing. The exception includes paper products, textiles, footwear,

plastic or glass products, miscellaneous, and art products. Third, in contrast, the mean of

imports per shipment is smallest in local currency invoicing. The exception in the case

of import frequency can be also applied to the case of imports per shipment. In those

industries, local currency invoicing has the largest imports per shipment.

=== Table 5 ===

Last, Table 6 reports the means of import frequency and imports per shipment by

exporter continent. In this table, the findings on import frequency are not so uniform

compared with the previous table for industries. Namely, its mean in local currency

invoicing is highest only in Asia and Europe. The frequency of import from the other

continents is not necessarily highest in local currency invoicing. This tendency will be

consistent with the findings in Table 3. Also, it might be worth noting that the mean

frequency of import from Asian countries is relatively higher in any invoice currencies.

This fact will indicate the significant importance of gravity factors (i.e., geographical

distance between trading partners) for frequency determination, as shown in Hornok and

Koren (2014). On the other hand, the mean of imports per shipment is consistently

largest in vehicle currency invoicing.

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=== Table 6 ===

4. Empirical Analysis Using the customs data in Thailand, this section empirically investigates

Propositions 1 and 2 specified in Section 2. We first examine how invoice currencies are

related to the frequency of shipments. Specifically, we estimate the following simple

equation.

ln 22

represents the frequency of firm f’s import of HS eight-digit

commodity p from country i in year t.5 is our main variable, which takes the

value one if invoice currency in the concerned transactions is THB and zero otherwise.

is a vector of time-variant import firm characteristics. is fixed effects for the

combination of export country, HS eight-digit commodity, and year. As shown in the

previous section, the mean of values per shipment differs by industries and exporter

continents. The above fixed effects will contribute to controlling for these differences.

We estimate this equation by ordinary least square (OLS) method.

As time-variant import firm characteristics, we introduce two variables. One is

firm f’s total imports from the world in year t. We expect that this variable is related to

the size of import firms. This variable is constructed by aggregating the customs data of

all imports by firms and years. The other is a dummy variable that takes the value one if

firm f gets engaged in exporting activities and zero otherwise. As demonstrated in the

previous studies on frequency, firms also optimize the frequency of export. Therefore,

exporters may have significantly different frequency of import. This variable is

constructed by employing the customs data on export. Those can be integrated by using

firm identification code as a key.6

There are two empirical issues. First, some firms import the same commodity

from the same country under multiple invoice currencies within a year. This observation

may be due to their import from different export firms or due to the change of invoice

currency during the year. Since we cannot identify either one, such observations, which

account for around 10% of all observations, are dropped from estimation sample.

Second, as demonstrated in Section 2, the invoice currency and the frequency are

simultaneously determined. Therefore, the error term is likely to be correlated with local

5 More exactly, as in the previous section, we also identify using the information on quantity unit, of which a subscript is omitted in this section. 6 The basic statistics for our variables are provided in Appendix B.

17

currency dummy variable. To correct this endogeneity bias, we also estimate the above

equation by instrument variable (IV) method. As an instrument, we use the share of

exports under local currency invoicing in total exports. In the case of non-exporters, this

share is set to zero. As demonstrated theoretically and empirically in Chung (2014),

invoice currency in import is highly correlated with that in export. However, the share

of exports under local currency will be not directly related with the frequency of import.

This variable is also constructed by employing the customs data on export.

Table 7 presents the baseline result on the relation between invoice currencies and

import frequency represented by the model (22). Columns (I) and (II) report the results

by the OLS. The models without and with a vector of time-variant import firm

characteristics are respectively estimated. The coefficient for Local Currency Dummy is

estimated to be significantly in both models. Specifically, the frequency of import

shipments is 9-14% higher when those shipments are invoiced in the local currency, i.e.

THB. As implied in Proposition 1, this result indicates that importers can make frequent

orders without consideration on the risk of exchange-rate fluctuations when transactions

are invoiced in their home currency. While the coefficient for Total Imports is

significantly positive, Exporter Dummy has a significantly negative coefficient. These

results indicate that the smaller-sized importers in terms of import values or the

exporters have the lower frequency of import transactions.

=== Table 7 ===

As mentioned above, the invoice currency and frequency of import transactions

are simultaneously determined. To correct the endogeneity biases from this simultaneity,

we estimate the model by the IV method and report the results in columns (III) and (IV).

The Cragg-Donald Wald F and Kleibergen-Paap Wald rk F test statistics, which are used

for the test of weak instruments as a null hypothesis, have significantly high values.

Also, in the first stage regression, the coefficients for THB Export Share are estimated

to be significantly positive, indicating that the importers who intensively use THB in

their exporting are likely to use THB also in importing, as is consistent with the results

in Chung (2014). The results in the second stage regression are qualitatively unchanged

with those reported in columns (I) and (II). The result in column (IV) shows the import

frequency is 26% higher when it is invoiced in the local currency.

We conduct some more robustness checks on the above results. First, we restrict

to import transactions that exist in the previous year. Since we count the number of

dates with import transactions from January to December in each year, we may

18

underestimate the frequency in firms who for the first time start to import on, say,

November. To avoid this underestimation, the estimation sample is restricted only to

import firm-export country-commodity observations that exist in the previous year. The

result is reported in column (I) in Table 8. Second, as explained in Section 3, we have

used import transaction data aggregated at a daily basis (called daily data). We relax this

restriction and define the raw number of import transactions as import frequency (called

full data). The result is provided in column (II). Both columns (I) and (II) show

qualitatively unchanged results with Table 7. Third, we differentiate non-local currency

between vehicle currency and producer currencies because the import frequency may be

systematically different between two kinds of currency. To do that, we introduce two

dummy variables on the use of those currencies as invoice currencies (Vehicle Currency

Dummy and Producer Currency Dummy) instead of Local Currency Dummy (and use

the daily data). The results by the OLS method are shown in columns (III) and (IV) and

show the significantly higher import frequency when the invoice currency is home

currency.

=== Table 8 ===

Next, we empirically investigate how invoice currencies are related to the average

values per shipment, i.e., Proposition 2. To do that, we again estimate the following

simple equation.

ln 23

represents the annual average values per shipment in firm f’s import of HS

eight-digit in year t, which is computed by dividing total annual imports by annual

import frequency. Due to our use of import values in dependent variable, import firm

characteristics do not include firms’ total imports. We use the daily data. The estimation

results by the OLS and the IV are reported in columns (I) and (II) in Table 9,

respectively. The coefficients for Local Currency Dummy are estimated to be

significantly negative, as implied in Proposition 2. Namely, the value per shipment is

significantly smaller for import transactions invoiced in the home currency. However,

the absolute magnitude of its coefficient looks too large in column (II), showing the

471% smaller average shipment of import transactions invoiced in local currency.

=== Table 9 ===

The other results are as follows. First, the coefficients for Exporter Dummy are

19

estimated to be significantly positive, indicating that the exporters have the larger

average shipment values of import transactions. Second, the estimation result of the

equation with Vehicle Currency Dummy and Producer Currency Dummy instead of

Local Currency Dummy is reported in column (III). Consistent with the result for Local

Currency Dummy, both those two dummy variables have the significantly positive

coefficients. Jointly with the above findings on the frequency, these contrasting results

between the frequency of import transactions and their average shipment values imply

that importers make just-in-time orders and shipments when the import transactions are

invoiced in their own currency. Our model provided a rationale for these results by

shedding the light on the role of the cost of exchange-rate risk management.

5. Benefits of an Invoice Currency In this section, using equations (17)-(21), we compute per-shipment fixed costs

for FCI management ( ). To do that, we need to specify three parameters, i.e., , , ,

and . The elasticity of substitution between varieties ( ) is obtained from Broda and

Weinstein (2006), which provides the elasticity at an HS three-digit level for Thailand.

We use the same values for gross ad valorem storage costs ( ) and discount rate ( ) as

Kropf and Sauré (2014), which are respectively 0.35 and 0.05. The forward premium

offered by a financial institution to each firm is an important parameter in our analysis

but unobservable. We simply assume that this parameter can be well approximated by

the change rate of exchange rates with THB from the current to the next year (average).

Therefore, this parameter is defined at an export country-year level.

Applying these parameters, we can compute , i.e., or , for each

import transaction. Importantly, we cannot observe both cases of HCI and FCI for each

transaction. Namely, we infer from import transactions under HCI and from

those under FCI. To obtain , we estimate the following simple equation.

24

We re-label subscripts for ; , , , and stand for import firm, export country, HS

eight-digit code, and year, respectively. is equal to for the case of HCI and

for the case of FCI. takes the value one if invoice currency in the

concerned transaction is foreign currency (i.e., not THB) and zero otherwise. In this

estimation, coefficient shows the fixed costs for FCI management ( ). We also

include year and export country fixed effects.

The estimation results are reported in Table 10. For rescale, we divide by one

thousand. Coefficient is shown as “Difference”. In column (I), we do not include

20

any fixed effects. The constant term is estimated to be 27, indicating that the average

per-import shipment fixed costs are 27 thousand THB (approximately 800 USD). This

magnitude is one-tenth of the average per-export shipment fixed costs in Swiss

(5,723CHF). Therefore, we may say that fixed costs per-import shipment are much

lower than those per-export shipment. The “difference” is 51, which indicates that the

average per-shipment fixed costs for FCI management are 51 thousand THB

(approximately 1,500 USD) and are around twice higher than the average per-import

shipment fixed costs. This magnitude of the difference is unchanged even if controlling

for year fixed effects, as shown in column (II). However, when controlling for export

country fixed effects, it becomes 119 thousand THB (approximately 3,600 USD). This

change will be because export country fixed effects control for the differences in

per-shipment import fixed costs ( ) across export countries and such differences are

significant.

=== Table 10 ===

Next, we examine the correlation of fixed costs for FCI management with export

country characteristics. To do that, by estimating equation (24) by export country (no

fixed effects), we infer those costs by export country, of which estimates are available in

Appendix. Then, we regress a log of those costs on various characteristics of export

country in year 2007. Those include gravity variables, namely, GDP, GDP per capita,

and the geographical distance with Thailand. Also, we introduce RTA dummy variable,

which takes the value one for partner countries of Thailand’s RTAs.7 Our main variable

in this estimation is Turnover Share, which is the share of the export country currency in

daily global foreign exchange market turnover, of which data are obtained from the BIS

Triennial Central Bank Survey in 2007. Currencies not listed in the survey are assigned

zero shares. As suggested in Goldberg and Tille (2011) and Chung (2014), its higher

share values will indicate the lower transaction costs for the export country currency.

The estimation results are reported in Table 11. Based on the high correlations

between GDP and GDP per capita and between Distance and RTA Dummy, we also

estimate the equations separately including those variables. There are three noteworthy

points. First, the export country’s GDP, GDP per capita, and distance with Thailand do

not affect the fixed costs for FCI management. Second, those costs are lower in

importing from RTA partners. The majority of the RTA partners are ASEAN plus three

7 As of 2007, RTA partners include ASEAN countries, Australia, China, India, Japan, and New Zealand. We also include Korea, with which the RTA was entry into force in 2010.

21

countries (i.e., China, Japan, and Korea). Among those countries, there exist some

currency cooperation schemes such as Chiang Mai Initiative. Therefore, the significant

correlation may come from the effects of such schemes rather than the effects of RTAs.

Last, as is consistent with our expectation, the higher turnover share of export country

currency is associated with the lower fixed costs for FCI management.

=== Table 11 ===

6. Concluding Remarks In this study, we examined the benefits of home currency invoicing, which are

represented by fixed costs of exchange rate risk management paid by importers when

they work with foreign currency invoicing. We revealed that those benefits for average

Thai importer are significantly positive, and range between 7.3% (1,500 USD) of

one-time shipment value and 17.1% (3,600 USD). Further, those benefits become larger

when Turnover Share is lower, or the export country is one of Thailand’s RTA partners.

It is also found that frequency of shipments is higher and the value per shipment is

smaller for import transactions priced in buyers’ currency than those not priced in it.

Our results propose clear qualitative policy implication on internationalization of

currencies from a microeconomic point of view.

22

Appendix A. Constancy of the Price Index

The price index is rewritten as

, A1

where

≡ , A2

≡ . A3

According to (7), prices of final goods which are produced at and sold at are

written as

1

∗

. A4

Thus, is rewritten as

1

1∗ . A5

For HCI importers, we proved that the equilibrium interval does not depend on , which

implies

∆ . A6

Thus,

1

1∗ ∆ 1 , A7

and does not depend on and constant over time.

Similarly, is rewritten as follows:

1

1∗ . A8

Arranging producer indices so that ∆ is increasing in . For 0, consider the

distribution where ∈ , is shipped at time . For given , the date of the

last shipment is derived as

∆ max ∈ | ∆ . A9

Here, is the set of integers. Suppose importers which make shipments at time . The

23

following relation holds for these importers:

∆ . A10

Define the solution by , , . The solution is uniquely determined as ∆ is

increasing in . Thus, the following relation holds for ∈ , , , 1, , ∩

Ω , where Ω is the set of FCI importers:

∆ , , ∆ , , . A11

Further,

∆ , , ∆ , , . A12

(A10) leads to lim → , , lim → 1, , , which jointly implies with

(A12) that does not depend on in this limit. According to (A8),

does not depend on and constant over time, and is so, too.

Appendix B. Other Tables

Table B1. Basic Statistics

Obs Mean Std. Dev. Min Max

ln Frequency (Daily) 4,980,162 0.5900 0.9095 0 5.8972

ln Frequency (Full) 4,980,162 0.8156 1.1362 0 12.0622ln Values 4,980,162 10.2618 2.4298 0 22.8140Local Currency Dummy 4,980,162 0.0601 0.2377 0 1ln Total Imports 4,980,162 17.6609 3.1781 2.5649 26.4119Exporter Dummy 4,980,162 0.6724 0.4693 0 1THB Export Share 4,980,162 0.0981 0.2571 0 1Vehicle Currency Dummy 4,980,162 0.5304 0.4991 0 1Producer Currency Dummy 4,980,162 0.4095 0.4917 0 1

Source: Authors’ computation

24

Table B2. Estimates on Fixed Costs by Export Country

Obs.Coef. S.D. Coef. S.D.

AFG 25 24 2 22 395

AGO 6,697 14,472 13 13,939 180ALB 6 13 9 13 159ARE 515 156 4 129 10,705ARG 533 307 14 300 3,646ARM 104 101 4 90 56ATG 70 242 1 240 70AUS 120 38 10 35 83,867AZE 14,496 19,180 3 17,824 88BDI 283 249 3 213 15BEN 585 769 13 765 95BGD 171 129 3 122 2,721BGR 86 102 5 98 2,328BHR 1,722 554 3 413 1,365BHS 77 79 3 78 76BIH 6 12 5 12 150BLR 1,958 1,956 1 1,862 160BLZ 841 2,147 67 2,033 251BMU -1 2 3 2 43BOL 54 63 15 61 85BRA 648 239 32 226 11,068BRB 165 680 0.2 674 56BRN 2,922 2,268 3 2,066 778BTN 2,374 1,389 2 1,091 141BWA 798 512 1 481 69CAN 114 64 15 62 29,634CHE 49 13 12 13 76,782CHL 175 186 42 183 1,810CHN 14 8 28 8 937,035CMR 358 773 8 757 359COG 385 211 4 176 146COK 207 597 6 586 28COL 317 722 4 700 1,000COM 38 15 3 9 13CRI 47 48 5 47 1,186CZE 41 12 9 12 13,914DJI 65 36 6 27 43DMA -9 76 85 73 73DNK 28 8 12 7 29,479

Difference Constant

Note: “Coef.”, “S.D.” and “Obs.” are coefficient estimate, standard deviation, and the number of

observations, respectively.

25

Table B2. Estimates on Fixed Costs by Export Country (Cont.)


DOM 18 20 21 19 688

DZA 27,889 24,927 208 22,641 80EGY 311 200 6 185 1,729ERI 54 55 0.4 50 41ETH 83 120 6 114 320FJI 134 54 2 45 165GAB 7,210 6,126 18 5,255 106GBR 53 11 9 11 155,685GEO 137 103 5 97 457GHA 354 171 3 160 190GIN 109 161 1 157 78GMB 101 97 2 91 80GTM 100 65 2 64 298HKG 35 16 9 16 105,788HND 18 20 14 20 290HRV 126 549 17 539 646HUN 71 63 6 57 8,644IDN 151 60 27 58 59,265IND 88 36 16 35 84,948IRN 1,521 835 10 759 1,231IRQ 2,208 7,532 3 7,395 305ISL 298 173 2 164 609ISR 77 48 16 48 14,906JAM 70 39 35 35 197JOR 181 134 6 123 553JPN 2 4 45 4 886,728KAZ 955 3,053 191 3,037 192KEN 34 13 3 12 1,033KGZ 5,395 7,659 14 6,434 34KHM -5 6 33 5 3,513KNA 36 133 9 128 14KOR 79 23 27 23 177,722KWT 2,427 774 3 627 1,114LAO -16 8 73 6 7,126LBN 101 62 9 55 312LBR 81 63 1 55 46LBY 38,538 35,732 3 30,565 41LKA 38 37 4 36 4,116LSO 37 108 14 105 34

Difference Constant



26



LTU 54 105 2 102 921

LVA 44 19 2 19 529MAC 11 21 10 20 759MAR 197 446 7 434 2,568MDA 4 6 3 6 223MDG 16 14 2 14 601MDV 345 98 2 86 331MEX 81 92 18 90 18,819MKD 33 100 1 99 174MLI 163 128 4 126 310MMR 3,374 1,831 15 1,075 7,085MNG 1,048 2,638 24 2,509 73MNP 182 258 5 214 19MOZ 249 125 1 122 294MRT 88 87 62 83 82MSR 15 39 7 38 16MUS 29 24 5 23 709MWI 320 246 3 237 76MYS 63 26 30 25 140,239NAM 188 183 9 176 236NCL 147 52 3 39 84NER 82 28 5 20 47NGA 1,559 1,528 4 1,433 898NIC 4 90 23 89 204NIU -2 2 3 2 45NOR 124 107 78 98 7,124NPL 10 10 3 10 1,347NRU 314 480 5 440 25NZL 81 35 6 33 13,263OMN 3,668 803 3 566 1,080PAK 110 106 10 101 6,018PAN 1,423 2,832 164 2,767 267PER 276 593 6 581 1,173PHL 139 101 16 97 26,986PNG 1,912 2,165 6 2,046 394POL 61 29 12 27 9,064PRI -246 71 349 67 767PYF -209 71 271 65 172QAT 3,374 934 23 676 1,689

Difference Constant



27



ROM 52 49 9 47 3,821

RUS 2,649 1,045 4 1,001 2,996SAU 2,287 635 6 488 3,970SDN 1,814 3,858 2 3,774 443SEN 297 200 8 190 140SGP 46 12 17 11 198,006SLB 1,473 2,249 7 2,240 123SLE 42 38 1 36 97SLV 276 1,747 14 1,745 372STP 115 446 1 440 35SUR 121 177 0.3 174 30SWE 70 16 16 15 41,393SWZ 108 112 67 106 1,820SYC 1,295 962 73 872 163SYR -5 47 72 41 137TCA 98 286 1 282 36TCD 1,807 4,516 14 4,370 47TGO 393 324 1 317 90TJK 344 244 22 207 25TKL 51 61 8 60 158TON 8 6 3 4 11TTO 148 126 1 121 81TUN 20 8 2 7 2,084TUR 165 231 9 224 14,888TUV 1,472 1,214 0 1,184 41TWN 3 7 40 7 311,303TZA 104 53 5 51 1,023UGA 155 75 3 70 150UKR 3,997 3,163 9 3,062 1,356URY -358 203 539 201 364USA 40 9 13 9 511,402VEN 881 893 4 760 213VGB 88 174 48 170 80VNM 101 61 12 59 35,566WLF 17 21 0.1 20 13WSM 64 181 7 178 34YEM 9,170 7,997 8 7,706 266ZAF 260 91 6 86 7,924ZMB 657 1,030 4 1,019 198

Difference Constant



28

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30

Table 1. The Decomposition of Import Transactions According to Invoice Currencies

Number/Value Share Number/Value Share Number/Value Share

Import Transactions

2007 315,220 0.07 1,810,002 0.39 2,518,534 0.542008 345,968 0.07 1,910,047 0.38 2,788,492 0.552009 354,500 0.07 1,678,244 0.35 2,762,252 0.582010 427,163 0.08 1,967,099 0.35 3,288,345 0.582011 479,208 0.08 2,017,907 0.34 3,406,764 0.58

Import Values (Million THB)2007 210,254 0.04 987,052 0.20 3,645,962 0.752008 220,038 0.04 1,082,668 0.19 4,467,971 0.772009 182,519 0.04 825,785 0.19 3,385,206 0.772010 255,404 0.05 1,042,832 0.18 4,340,484 0.772011 289,262 0.04 1,196,530 0.18 5,129,073 0.78

Local Producer Vehicle


Table 2. Import Frequency and Imports per Shipment by Invoice Currency and Industry

Frequency Value Frequency Value Frequency Value

Live animals 0.08 0.02 0.45 0.19 0.47 0.79

Vegetable products 0.07 0.02 0.24 0.22 0.69 0.76Animal/vegetable fats and oils 0.03 0.02 0.45 0.08 0.52 0.90Food products 0.05 0.10 0.47 0.20 0.49 0.71Mineral products 0.08 0.00 0.40 0.01 0.51 0.98Chemical products 0.10 0.10 0.37 0.18 0.53 0.72Plastics and rubber 0.07 0.05 0.38 0.30 0.55 0.65Leather products 0.07 0.06 0.18 0.21 0.75 0.73Wood products 0.14 0.05 0.34 0.18 0.52 0.77Paper products 0.08 0.03 0.39 0.21 0.53 0.76Textiles 0.07 0.02 0.19 0.20 0.74 0.77Footwear 0.04 0.02 0.17 0.05 0.79 0.93Plastic or glass products 0.08 0.04 0.37 0.35 0.54 0.61Precision metals 0.03 0.00 0.30 0.11 0.66 0.88Base Metal 0.09 0.03 0.41 0.18 0.50 0.80Machinery 0.08 0.06 0.31 0.25 0.61 0.69Transport equipment 0.11 0.12 0.40 0.34 0.49 0.54Precision machinery 0.08 0.09 0.43 0.41 0.49 0.50Miscellaneous 0.07 0.05 0.24 0.30 0.68 0.66Art products 0.26 0.12 0.31 0.47 0.43 0.42



31

Table 3. Import Frequency and Imports per Shipment by Invoice Currency and Exporter

Continent

Frequency Value Frequency Value Frequency Value

Africa 0.10 0.01 0.01 0.00 0.89 0.99

America 0.06 0.04 0.77 0.65 0.17 0.31Asia 0.07 0.04 0.25 0.12 0.69 0.84Europe 0.14 0.08 0.49 0.29 0.37 0.64Pacific 0.09 0.01 0.50 0.06 0.41 0.93



Table 4. Basic Statistics for Import Frequency and Imports per Shipment by Invoice

Currency

Mean S.D. Median Maximum

Frequency

Local 5.559 16.224 1 331Producer 4.227 11.324 1 365Vehicle 5.174 14.359 1 364Total 4.831 13.416 1 365

Values per shipment (Thousand THB)Local 282 2,494 10 254,875Producer 421 6,169 27 1,961,622Vehicle 947 25,995 37 7,805,302Total 695 19,479 30 7,805,302


32

Table 5. The Means of Import Frequency and Imports per Shipment by Invoice

Currency and Industry

Local Producer Vehicle Local Producer Vehicle

Live animals 10.718 9.728 5.289 301 1,515 3,124

Vegetable products 11.613 5.922 8.768 493 1,421 1,658Animal/vegetable fats and oils 4.878 3.913 4.439 436 262 2,681Food products 7.496 4.045 4.416 1,099 354 1,466Mineral products 7.718 3.987 4.663 1,117 762 45,256Chemical products 9.858 4.577 4.761 1,065 439 1,407Plastics and rubber 9.533 5.761 5.746 192 171 306Leather products 7.406 2.624 5.017 111 98 226Wood products 6.634 4.926 3.516 129 178 524Paper products 2.764 3.091 3.926 39 90 274Textiles 2.110 2.917 5.145 45 150 304Footwear 4.213 2.240 5.315 46 39 123Plastic or glass products 2.174 3.845 4.085 37 200 248Precision metals 7.933 6.181 5.315 453 1,640 4,886Base Metal 8.488 4.914 4.784 169 246 971Machinery 5.871 3.952 5.709 380 621 509Transport equipment 10.516 6.104 6.319 672 829 1,707Precision machinery 6.153 3.455 3.903 467 344 315Miscellaneous 1.776 2.495 3.806 40 126 153Art products 1.013 1.155 1.302 12 119 85

Frequency Values per shipment (Thousand THB)


Table 6. The Means of Import Frequency and Imports per Shipment by Invoice

Currency and Exporter Continent Local Producer Vehicle

Frequency

Africa 2.835 1.309 4.218America 3.102 4.001 3.569Asia 6.267 5.304 5.576Europe 6.110 3.219 3.805Pacific 1.313 2.924 3.413

Values per shipment (Thousand THB)Africa 101 38 4,703America 183 492 1,599Asia 364 426 884Europe 215 380 688Pacific 75 311 4,009


33

Table 7. Determinants of Import Frequency

(I) (II) (III) (IV)

Second stage

Local Currency Dummy 0.0893*** 0.1328*** 0.6000*** 0.2310***[0.0023] [0.0022] [0.0133] [0.0124]

ln Total Imports 0.0763*** 0.0766***[0.0002] [0.0002]

Exporter Dummy -0.0618*** -0.0620***[0.0011] [0.0010]

Number of Observations 4,980,162 4,980,162 4,744,619 4,744,619R-squared (Centered) 0.1948 0.2354 -0.0131 0.050

First stageTHB Export Share 0.1293*** 0.1418***

[0.0006] [0.0006]Centered R-squared 0.027 0.0321Cragg-Donald Wald F 1.20E+05 1.40E+05Kleibergen-Paap Wald rk F 44547.07 5.10E+04

IVOLS

Notes: The dependent variable is a log of the frequency of import shipments. The parentheses are

robust standard errors. ***, **, and * indicate 1%, 5%, and 10% significance, respectively. We

include fixed effects for the combination of export country, HS eight-digit commodity, and year. In

the results for the first stage regression of IV method, we report the result of the excluded variable,

THB Export Share, which is the share of exports under local currency invoicing in total exports.

Various test statistics are also presented.

34

Table 8. Robustness Checks on Import Frequency

(I) (II) (III) (IV)Estimation Method IV IV OLS OLSData type Daily Full Daily Daily

Second stage

Local Currency Dummy 0.2870*** 0.2804***[0.0202] [0.0156]

Vehicle Currency Dummy -0.0743*** -0.1511***[0.0024] [0.0024]

Producer Currency Dummy -0.1006*** -0.1193***[0.0023] [0.0023]

ln Total Imports 0.0930*** 0.0905*** 0.0766***[0.0004] [0.0002] [0.0002]

Exporter Dummy -0.0633*** -0.0616*** -0.0620***[0.0021] [0.0013] [0.0011]

Number of Observations 1,632,779 4,744,619 4,980,162 4,980,162R-squared (Centered) 0.0444 0.045 0.1948 0.2355

First stageTHB Export Share 0.1657*** 0.1418***

[0.0011] [0.0006]Centered R-squared 0.0283 0.0321Cragg-Donald Wald F 9.08E+04 1.40E+05Kleibergen-Paap Wald rk F 24713.77 51233.44

Notes: The dependent variable is a log of the frequency of import shipments. The parentheses are

robust standard errors. ***, **, and * indicate 1%, 5%, and 10% significance, respectively. We





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Table 9. Determinants of Average Import Values per Shipment

(I) (II) (III)Estiamtion Method OLS IV OLS

Second stage

Local Currency Dummy -0.5679*** -1.7425***[0.0050] [0.0291]

Vehicle Currency Dummy 0.6341***[0.0054]

Producer Currency Dummy 0.5179***[0.0052]

Exporter Dummy 0.3248*** 0.3144*** 0.3215***[0.0024] [0.0023] [0.0024]

Number of Observations 4,980,162 4,806,105 4,980,162R-squared (Centered) 0.3948 -0.0054 0.3950

First stageTHB Export Share 0.1431***

[0.0006]Centered R-squared 0.0312Cragg-Donald Wald F 1.40E+05Kleibergen-Paap Wald rk F 52865.43

Notes: The dependent variable is a log of the average import values per shipment. The parentheses

are robust standard errors. ***, **, and * indicate 1%, 5%, and 10% significance, respectively. We





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Table 10. Fixed Costs for FCI Management

(I) (II) (III)

Difference 51.0769*** 50.8037*** 119.2677***

[7.1360] [7.1412] [8.0531]Constant 26.5147***

[6.8806]Year Dummy NO YES YESExport Country Dummy NO NO YESNumber of Observations 4,171,649 4,171,649 4,171,649

Notes: The parentheses are standard errors. *** indicate 1% significance.

Table 11. Correlation of Fixed Costs for FCI Management with Export Country

Characteristics

(I) (II) (III) (IV) (V)

ln GDP 0.0524 0.0618 0.0549 0.0102

[0.0915] [0.0793] [0.0915] [0.0865]ln GDP per capita 0.027 0.0622 0.0213 0.0256

[0.1184] [0.1015] [0.1145] [0.1147]ln Distance -0.1797 -0.1876 -0.1749 0.0722

[0.3196] [0.3159] [0.3115] [0.2483]RTA Dummy -1.2538* -1.1481* -1.2528* -1.0567*

[0.6827] [0.6182] [0.6825] [0.5540]Turnover Share -6.3278** -5.7026** -6.2606** -6.6050** -6.2948**

[2.7204] [2.4059] [2.6892] [2.7919] [3.1262]Constant 5.1765 6.2658** 5.1945 3.5555* 3.9992

[3.4003] [2.7313] [3.4133] [2.1429] [3.2014]Number of Observations 137 137 137 137 137R-squared 0.0402 0.0376 0.0398 0.0375 0.0159

Notes: The parentheses are robust standard errors. ***, **, and * indicate 1%, 5%, and 10%

significance, respectively.

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Figure 1. Structure of the Model (Simplified Three-Country Example)

Figure 2. Import Frequency and Invoice Currency

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Figure 3. Sample Distribution of Number of Shipments per Year

Source: Authors’ compilation

02

46

De

nsity

0 2 4 6Number of Shipments per Year, logged

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Figure 4. Sample Distribution of Average Import Values per Shipment

Source: Authors’ compilation

0.0

5.1

.15

.2D

ens

ity

0 5 10 15 20 25Average Values in THB, logged

benefits of home currency invoicing -...

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